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Data mining of environmental stress tolerances on plants

Richard S. Segall (Department of Computer and Information Technology, College of Business, Arkansas State University, Jonesboro, Arkansas, USA)
Gauri S. Guha (Department of Economics and Finance, College of Business, Arkansas State University, Jonesboro, Arkansas, USA)
Sarath A. Nonis (Department of Marketing and Management, College of Business, Arkansas State University, Jonesboro, Arkansas, USA)

Kybernetes

ISSN: 0368-492X

Article publication date: 15 February 2008

Abstract

Purpose

This paper seeks to present a complete set of graphical and numerical outputs of data mining performed for microarray databases of plant data as described in earlier research by the authors. A brief description of data mining is also presented, as well as a brief background of previous research.

Design/methodology/approach

The paper uses applications of data mining using SAS Enterprise Miner Version 4 for plant data from the Osmotic Stress Microarray Information Database (OSMID) that is available on the web for both normalized and log(2) transformed data.

Findings

This paper illustrates that useful information about the effects of environmental stress tolerances (ESTs) on plants can be obtained by using data mining.

Research limitations/implications

Use of SAS Enterprise Miner was very effective for performing data mining of microarray databases with its modules of cluster analysis, decision trees, and descriptive and visual statistics.

Practical implications

The data used from the OSMID database are considered to be representative of those that could be used for biotech application such as the manufacture of plant‐made‐pharmaceuticals and genetically modified foods.

Originality/value

This paper contributes to the discussion on the use of data mining for microarray databases and specifically for studying the effects of ESTs on plants.

Keywords

Citation

Segall, R.S., Guha, G.S. and Nonis, S.A. (2008), "Data mining of environmental stress tolerances on plants", Kybernetes, Vol. 37 No. 1, pp. 127-148. https://doi.org/10.1108/03684920810851041

Publisher

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Emerald Group Publishing Limited

Copyright © 2008, Emerald Group Publishing Limited